import nltk
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')
from nltk.corpus import stopwords
nltk.download("all")
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
!unzip /usr/share/nltk_data/corpora/wordnet.zip -d /usr/share/nltk_data/corpora/
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
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5.1.1 [Logistics Regression with CountVectorizer](#5.1.1-Logistics-Regression-with-CountVectorizer)<br>
5.1.2 [Logistics Regression with TFIDFVectorizer](#5.1.2-Logistics-Regression-with-TFIDFVectorizer)<br>
5.1.3 [Logistics Regression with Word2Vec](#5.1.3-Logistics-Regression-with-Word2Vec)<br>
5.1.4 [Logistics Regression with GoogleNews Word2Vec](#5.1.4-Logistics-Regression-with-GoogleNews-Word2Vec)<br>
5.2.1 [SGD Classifier & GridSearchCV with CountVectorizer](#5.2.1-SGD-Classifier-&-GridSearchCV-with-CountVectorizer)<br>
5.2.2 [SVM Classifier with TFIDFVectorizer](#5.2.2-SVM-Classifier-with-TFIDFVectorizer)<br>
5.2.3 [SVM Classifier & GridSearchCV with Word2Vec](#5.2.3-SVM-Classifier-&-GridSearchCV-with-Word2Vec)<br>
5.2.4 [SGD Classifier with GoogleNews Word2Vec](#5.2.4-SGD-Classifier-with-GoogleNews-Word2Vec)<br>
5.3.1 [Random Forest with CountVectorizer](#5.3.1-Random-Forest-with-CountVectorizer)<br>
5.3.2 [Random Forest with TFIDFVectorizer](#5.3.2-Random-Forest-with-TFIDFVectorizer)<br>
5.3.3 [Random Forest with Word2Vec](#5.3.3-Random-Forest-with-Word2Vec)<br>
5.3.4 [Random Forest with GoogleNews Word2Vec](#5.3.4-Random-Forest-with-GoogleNews-Word2Vec)<br># Importing the libraries
import pandas as pd
# Importing the dataset
# Define the column names for the data
dataset = pd.read_csv('/kaggle/input/multilingual-lyrics-for-genre-classification/train.csv')
df = pd.DataFrame(dataset)
df.head()
| Artist | Song | Genre | Language | Lyrics | |
|---|---|---|---|---|---|
| 0 | 12 stones | world so cold | Rock | en | It starts with pain, followed by hate\nFueled ... |
| 1 | 12 stones | broken | Rock | en | Freedom!\nAlone again again alone\nPatiently w... |
| 2 | 12 stones | 3 leaf loser | Rock | en | Biting the hand that feeds you, lying to the v... |
| 3 | 12 stones | anthem for the underdog | Rock | en | You say you know just who I am\nBut you can't ... |
| 4 | 12 stones | adrenaline | Rock | en | My heart is beating faster can't control these... |
df.columns
Index(['Artist', 'Song', 'Genre', 'Language', 'Lyrics'], dtype='object')
df.shape
(290183, 5)
df.describe()
| Artist | Song | Genre | Language | Lyrics | |
|---|---|---|---|---|---|
| count | 290183 | 290182 | 290183 | 290179 | 290148 |
| unique | 11152 | 164357 | 10 | 33 | 249297 |
| top | elvis presley | intro | Rock | en | Instrumental |
| freq | 1611 | 163 | 121404 | 250197 | 540 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 290183 entries, 0 to 290182 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Artist 290183 non-null object 1 Song 290182 non-null object 2 Genre 290183 non-null object 3 Language 290179 non-null object 4 Lyrics 290148 non-null object dtypes: object(5) memory usage: 11.1+ MB
df['Artist'].value_counts()
elvis presley 1611
chris brown 1239
elvis costello 923
ella fitzgerald 874
the rolling stones 820
...
the gregory brothers 1
flamingokvintetten 1
fjeld 1
danceplaycreate 1
crawdad republic 1
Name: Artist, Length: 11152, dtype: int64
len(df['Artist'].unique())
11152
df['Genre'].value_counts()
Rock 121404 Pop 108714 Metal 20291 Jazz 13545 Folk 8644 Indie 8449 R&B 2793 Hip-Hop 2240 Electronic 2213 Country 1890 Name: Genre, dtype: int64
len(df['Genre'].unique())
10
df['Language'].value_counts()
en 250197 pt 30102 es 3892 ro 1184 it 808 id 737 fr 644 de 478 sw 304 tl 241 so 229 cy 226 ca 137 tr 116 nl 116 sk 98 hr 97 no 93 sl 77 af 77 da 71 sv 61 et 58 fi 54 pl 24 cs 17 sq 15 hu 10 vi 7 ru 4 lt 2 lv 2 ko 1 Name: Language, dtype: int64
len(df['Language'].unique())
34
df.isna().sum()
Artist 0 Song 1 Genre 0 Language 4 Lyrics 35 dtype: int64
df.dropna(inplace=True)
df['Lyrics'].isna().sum()
0
df.isna().sum()
Artist 0 Song 0 Genre 0 Language 0 Lyrics 0 dtype: int64
df.shape
(290143, 5)
int(df.shape[0]/20)
14507
num_samples_per_class = 5000
sliced_df = pd.DataFrame(columns=df.columns)
unique_classes = df['Genre'].unique()
for class_label in unique_classes:
class_df = df[df['Genre'] == class_label]
if len(class_df) >= num_samples_per_class:
class_samples = class_df.sample(n=num_samples_per_class, random_state=42)
else:
class_samples = class_df # Use all available samples if there are fewer than num_samples_per_class
sliced_df = pd.concat([sliced_df, class_samples])
df = sliced_df.copy()
df.shape
(39136, 5)
df['Genre'].value_counts()
Rock 5000 Metal 5000 Pop 5000 Indie 5000 Folk 5000 Jazz 5000 R&B 2793 Hip-Hop 2240 Electronic 2213 Country 1890 Name: Genre, dtype: int64
df = df[df['Genre'].isin(['Rock', 'Jazz', 'Hip-Hop', 'Metal', 'Country'])]
df['Genre'].value_counts()
Rock 5000 Metal 5000 Jazz 5000 Hip-Hop 2240 Country 1890 Name: Genre, dtype: int64
# Preprocessing the data using NLTK
# Importing the libraries
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
df.shape
(19130, 5)
# Initialize the lemmatizer
lemmatizer = WordNetLemmatizer()
# Defining a function to tokenize and lemmetize the text
def tokenize_and_lemmatize(text):
tokens = word_tokenize(text)
lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]
return " ".join(lemmatized_tokens)
# Applying the function to the text column
df['Lyrics'] = df['Lyrics'].apply(tokenize_and_lemmatize)
# Data Cleansing: Remove stopwords, remove symbols, remove URLs
# Importing the libraries
import re
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
# Defining a function to clean the text
def clean_text(text):
# Remove URLs
text = re.sub(r'http\S+', '', text)
# Remove symbols and numbers
text = re.sub(r'[^\w\s]', '', text)
# Remove stopwords
text = " ".join([word for word in text.split() if word.lower() not in stop_words])
return text
# Applying the clean text function to the text column
df['Lyrics'] = df['Lyrics'].apply(clean_text)
# Displaying the first 5 rows of the dataset
df.head()
| Artist | Song | Genre | Language | Lyrics | |
|---|---|---|---|---|---|
| 256544 | mott the hoople | pearl 'n' roy (england) | Rock | en | shut clean chimney kid 1974 shake fist make ol... |
| 145115 | the rolling stones | back of my hand | Rock | en | hear preacher corner Ranting like crazy man sa... |
| 8907 | bryan adams | is your mama gonna miss ya? | Rock | en | might well bin blind Thought owned world time ... |
| 141431 | peter bjorn and john | may seem macabre | Rock | en | darkenwide misty eye say five break night eye ... |
| 33998 | mana | róbame el alma | Rock | es | robame el alma bebela embriagate hasta caer am... |
# Using countvectorizer to convert text to vectors
from sklearn.feature_extraction.text import CountVectorizer
# Initialize CountVectorizer
count_vectorizer = CountVectorizer()
# Fit and transform the text column
count_vectors = count_vectorizer.fit_transform(df['Lyrics'])
# Print the shape and a small sample of the output
print("Shape of CountVectorizer output:", count_vectors.shape)
print("Sample of CountVectorizer output:\n", count_vectors[0:5])
Shape of CountVectorizer output: (19130, 69061) Sample of CountVectorizer output: (0, 54980) 1 (0, 12066) 1 (0, 11429) 1 (0, 33600) 1 (0, 264) 1 (0, 54297) 1 (0, 23492) 1 (0, 37066) 2 (0, 42962) 1 (0, 63205) 1 (0, 66424) 1 (0, 2223) 2 (0, 42259) 3 (0, 46711) 1 (0, 67667) 2 (0, 39013) 1 (0, 22255) 1 (0, 35687) 1 (0, 66350) 1 (0, 41493) 1 (0, 25890) 2 (0, 7573) 1 (0, 7549) 1 (0, 25159) 1 (0, 42323) 3 : : (4, 20352) 1 (4, 61167) 1 (4, 48634) 1 (4, 46660) 1 (4, 6866) 1 (4, 20035) 3 (4, 21314) 4 (4, 31837) 3 (4, 24955) 3 (4, 55078) 1 (4, 20813) 2 (4, 44871) 1 (4, 13916) 1 (4, 34390) 1 (4, 42266) 1 (4, 15768) 1 (4, 44561) 1 (4, 16907) 1 (4, 15014) 1 (4, 35912) 2 (4, 42876) 1 (4, 37458) 1 (4, 16908) 2 (4, 15015) 2 (4, 61537) 2
# Using TF-IDF to convert text to vectors
from sklearn.feature_extraction.text import TfidfVectorizer
# Initialize TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer()
# Fit and transform the text column
tfidf_vectors = tfidf_vectorizer.fit_transform(df['Lyrics'])
# Print the shape and a small sample of the output
print("Shape of TFIDFVectorizer output:", tfidf_vectors.shape)
print("Sample of TFIDFVectorizer output:\n", tfidf_vectors[0:5])
Shape of TFIDFVectorizer output: (19130, 69061) Sample of TFIDFVectorizer output: (0, 58009) 0.047784765123869415 (0, 39072) 0.06177086187245119 (0, 54328) 0.06398968124415798 (0, 13703) 0.03613040631108714 (0, 9463) 0.019003846356325203 (0, 34625) 0.08219135471602551 (0, 33971) 0.05197233025201602 (0, 27206) 0.05905048693610614 (0, 55421) 0.08837602843935535 (0, 54889) 0.053562731170563274 (0, 60228) 0.04389923385057285 (0, 22423) 0.047558596787565174 (0, 11358) 0.05584967419216082 (0, 66934) 0.03393440902681408 (0, 6874) 0.029496042460212396 (0, 7555) 0.04831803468641895 (0, 67254) 0.033231784462485016 (0, 1426) 0.0747831525819462 (0, 61311) 0.016881504600495197 (0, 26453) 0.0365194499910823 (0, 60872) 0.02135534645414828 (0, 60370) 0.03929409639958651 (0, 28803) 0.05299711662554344 (0, 10344) 0.04173449488650284 (0, 36251) 0.027496076851097322 : : (4, 20035) 0.11828044916790567 (4, 6866) 0.030905247686823015 (4, 46660) 0.03587313575799543 (4, 48634) 0.016900905395643172 (4, 61167) 0.026171734027163374 (4, 20352) 0.02507193460509276 (4, 2503) 0.027351567055516546 (4, 28236) 0.025528736445325776 (4, 45779) 0.03456818436139286 (4, 9484) 0.03942681638930189 (4, 36631) 0.1287520790548046 (4, 62912) 0.2643157223533351 (4, 13070) 0.2379129537983728 (4, 58681) 0.035166032038062456 (4, 38711) 0.14227586942292322 (4, 39561) 0.07885363277860379 (4, 20639) 0.03942681638930189 (4, 2812) 0.03942681638930189 (4, 9574) 0.03587313575799543 (4, 28125) 0.06563047103330662 (4, 20221) 0.03942681638930189 (4, 6157) 0.03942681638930189 (4, 2672) 0.49714459900928626 (4, 19944) 0.44316821681745516 (4, 51448) 0.5125486130609246
# Using word2vec to convert text to vectors
from gensim.models import Word2Vec
# Tokenize the text column
tokenized_text = df['Lyrics'].apply(lambda x: x.split())
# Train a Word2Vec model
word2vec_model = Word2Vec(tokenized_text, window=5, min_count=1, workers=4)
word2vec_model.train(tokenized_text, total_examples=len(tokenized_text), epochs=10)
# Create Word2Vec vectors for the text column
word2vec_vectors = tokenized_text.apply(lambda x: [word2vec_model.wv[word] for word in x])
# Print the shape and a small sample of the output
print("Sample of Word2Vec output:", word2vec_vectors.head())
Sample of Word2Vec output: 256544 [[-1.1274967, -0.23310012, -1.1081858, 0.88278... 145115 [[-0.7397139, 0.4824181, -3.3763597, -2.000244... 8907 [[1.0459611, -0.36676323, 0.91696805, 1.696775... 141431 [[-0.013266287, -0.030382048, -0.0126411095, -... 33998 [[-0.2751627, 0.17716298, -0.003995462, -0.214... Name: Lyrics, dtype: object
# Using GoogleNews word2vec to convert text to vectors
from gensim.models import KeyedVectors
# Load the GoogleNews Word2Vec model
google_w2v_path = '/kaggle/input/googlenewsvectorsnegative300/GoogleNews-vectors-negative300.bin'
google_w2v_model = KeyedVectors.load_word2vec_format(google_w2v_path, binary=True)
# Create GoogleNews Word2Vec vectors for the text column
google_w2v_vectors = tokenized_text.apply(lambda x: [google_w2v_model[word] for word in x if word in google_w2v_model])
# Print the shape and a small sample of the output
print("Sample of GoogleNews Word2Vec output:", google_w2v_vectors.head())
Sample of GoogleNews Word2Vec output: 256544 [[-0.14160156, 0.024536133, 0.49023438, -0.016... 145115 [[0.06689453, -0.19726562, 0.12988281, 0.05029... 8907 [[0.22753906, 0.12011719, 0.068359375, 0.32421... 141431 [[0.29101562, 0.17675781, 0.037109375, -0.1083... 33998 [[-0.012023926, 0.20410156, 0.22949219, -0.009... Name: Lyrics, dtype: object
y = df['Genre']
y.value_counts()
Rock 5000 Metal 5000 Jazz 5000 Hip-Hop 2240 Country 1890 Name: Genre, dtype: int64
from sklearn.preprocessing import LabelEncoder
# Encode genre labels
label_encoder = LabelEncoder()
genre_encoded = label_encoder.fit_transform(y)
unique_labels = df['Genre'].unique()
unique_encoded = label_encoder.transform(unique_labels)
# Create a DataFrame to display the unique labels and their encoded values
df_encoded = pd.DataFrame({'Unique Labels': unique_labels, 'Encoded Labels': unique_encoded})
# Print the DataFrame
df_encoded
| Unique Labels | Encoded Labels | |
|---|---|---|
| 0 | Rock | 4 |
| 1 | Metal | 3 |
| 2 | Jazz | 2 |
| 3 | Hip-Hop | 1 |
| 4 | Country | 0 |
# Logistic Regression with CountVectorizer
# Importing the libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(count_vectors,genre_encoded, test_size=0.2, random_state=42)
# Initialize and train the Logistic Regression model
lr_model_count = LogisticRegression(max_iter=1000)
lr_model_count.fit(X_train, y_train)
# Make predictions and print the classification report
y_pred_count = lr_model_count.predict(X_test)
print("Classification Report for Logistic Regression with CountVectorizer:\n", classification_report(y_test, y_pred_count))
Classification Report for Logistic Regression with CountVectorizer:
precision recall f1-score support
0 0.38 0.37 0.37 362
1 0.78 0.70 0.74 431
2 0.67 0.74 0.70 999
3 0.69 0.67 0.68 1039
4 0.47 0.45 0.46 995
accuracy 0.61 3826
macro avg 0.60 0.59 0.59 3826
weighted avg 0.61 0.61 0.61 3826
# Logistic Regression with TFIDFVectorizer
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(tfidf_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize and train the Logistic Regression model
lr_model_tfidf = LogisticRegression(max_iter=1000)
lr_model_tfidf.fit(X_train, y_train)
# Make predictions and print the classification report
y_pred_tfidf = lr_model_tfidf.predict(X_test)
print("Classification Report for Logistic Regression with TFIDFVectorizer:\n", classification_report(y_test, y_pred_tfidf))
Classification Report for Logistic Regression with TFIDFVectorizer:
precision recall f1-score support
0 0.54 0.24 0.33 362
1 0.85 0.70 0.77 431
2 0.67 0.73 0.70 999
3 0.70 0.77 0.73 1039
4 0.50 0.55 0.52 995
accuracy 0.64 3826
macro avg 0.65 0.60 0.61 3826
weighted avg 0.64 0.64 0.64 3826
# Logistic Regression with Word2Vec
# Importing the libraries
import numpy as np
# Function to calculate the mean vector for each document
def mean_vector(words):
# Filter out words that are not in the Word2Vec model's vocabulary
valid_words = [word for word in words if word in word2vec_model.wv]
if valid_words:
# Calculate the mean vector for valid words
vectors = [word2vec_model.wv[word] for word in valid_words]
return np.mean(vectors, axis=0)
else:
# Return a zero vector if no valid words are found
return np.zeros(word2vec_model.vector_size)
# Apply the function to the tokenized text
word2vec_mean_vectors = np.array(tokenized_text.apply(mean_vector).tolist())
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(word2vec_mean_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize and train the Logistic Regression model
lr_model_word2vec = LogisticRegression(max_iter=1000)
lr_model_word2vec.fit(X_train, y_train)
# Make predictions and print the classification report
y_pred_word2vec = lr_model_word2vec.predict(X_test)
print("Classification Report for Logistic Regression with Word2Vec:\n", classification_report(y_test, y_pred_word2vec))
Classification Report for Logistic Regression with Word2Vec:
precision recall f1-score support
0 0.28 0.11 0.16 362
1 0.70 0.69 0.70 431
2 0.63 0.69 0.65 999
3 0.67 0.72 0.70 1039
4 0.45 0.48 0.46 995
accuracy 0.59 3826
macro avg 0.55 0.54 0.53 3826
weighted avg 0.57 0.59 0.57 3826
# Function to calculate the mean vector for each document
def mean_vector_google_w2v(words):
# Filter out words that are not in the GoogleNews Word2Vec model's vocabulary
valid_words = [word for word in words if word in google_w2v_model]
if valid_words:
# Calculate the mean vector for valid words
vectors = [google_w2v_model[word] for word in valid_words]
return np.mean(vectors, axis=0)
else:
# Return a zero vector if no valid words are found
return np.zeros(google_w2v_model.vector_size)
# Apply the function to the tokenized text
google_w2v_mean_vectors = np.array(tokenized_text.apply(mean_vector_google_w2v).tolist())
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(google_w2v_mean_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize and train the Logistic Regression model
lr_model_google_w2v = LogisticRegression(max_iter=1000)
lr_model_google_w2v.fit(X_train, y_train)
# Make predictions and print the classification report
y_pred_google_w2v = lr_model_google_w2v.predict(X_test)
print("Classification Report for Logistic Regression with GoogleNews Word2Vec:\n", classification_report(y_test, y_pred_google_w2v))
Classification Report for Logistic Regression with GoogleNews Word2Vec:
precision recall f1-score support
0 0.50 0.19 0.28 362
1 0.76 0.69 0.72 431
2 0.64 0.71 0.67 999
3 0.69 0.75 0.72 1039
4 0.48 0.50 0.49 995
accuracy 0.62 3826
macro avg 0.61 0.57 0.58 3826
weighted avg 0.61 0.62 0.61 3826
# Importing the SGD Classifier
from sklearn.linear_model import SGDClassifier
# Split the data into training and testing sets and training the model
X_train, X_test, y_train, y_test = train_test_split(count_vectors, genre_encoded, test_size=0.2, random_state=42)
sgd_model_count = SGDClassifier(loss='hinge', max_iter=10000)
sgd_model_count.fit(X_train, y_train)
# Make predictions and printing the classification report
y_pred_count_sgd = sgd_model_count.predict(X_test)
print("Classification Report for SGD Classifier with CountVectorizer:\n", classification_report(y_test, y_pred_count_sgd))
Classification Report for SGD Classifier with CountVectorizer:
precision recall f1-score support
0 0.39 0.37 0.38 362
1 0.71 0.78 0.74 431
2 0.68 0.71 0.69 999
3 0.69 0.64 0.66 1039
4 0.45 0.46 0.46 995
accuracy 0.60 3826
macro avg 0.58 0.59 0.59 3826
weighted avg 0.60 0.60 0.60 3826
# Check the distribution of the target values
target_distribution = df['Lyrics'].value_counts()
print(target_distribution)
Instrumental 43
heart sad lonely sigh dear nt seen body soul spend day longing wondering wronging tell mean body soul ca nt believe hard conceive turn away romance pretending look like ending Unless could one chance prove dear life wreck making know taking gladly surrender body soul life wreck making know taking gladly surrender body soul 39
hate see evening sun go hate see evening sun go Cause baby gone left town Feelin tomorrow like feel today feelin tomorrow like feel today pack truck make giveaway St Louis woman diamond ring Pulls man around wa nt man love would gone nowhere nowhere got St Louis blue blue man got heart like rock cast sea else would nt gone far love baby like school boy love pie Like Kentucky colonel love mint n rye love man till day die 19
nt know love Til learned meaning blue loved love lose nt know love nt know lip hurt kissed pay cost flipped heart lost nt know love know lost heart fear thought reminiscing lip taste tear Lose taste kissing nt know heart burn love live yet never dy faced dawn sleepless eye nt know love 18
Speak low speak love summer day withers away soon soon Speak low speak love moment swift like ship adrift swept apart soon Speak low darling speak low Love spark lost dark soon soon feel wherever go tomorrow near tomorrow always soon Time old love brief Love pure gold time thief late darling late curtain descends evrything end soon soon wait darling wait speak low Speak love soon 18
..
Years ago came back ha hardest attack burning fire brain could feel deadly flame Tales magic head dark dream fall mind hold key fortune feel strange thing mind stone Ambar must find take chance day Chorus Halloween wizard crown take Halloween Halloween return Halloween servant Blind cross realm death gate open wide close dungeon deep place hide time changed ha fight magic spell fall Illusions falling mind key find ritual ha begun Chorus battle ha begun warlock time ha come pay price pay nt see sign loosing life die forever life Halloween wizard crown take Halloween Halloween return Halloween Halloween wizard crown take 1
fragile game play ghost yesterday ca nt let go never say goodbye trace remains stone mark graf memory thought could deny much lose pain put carelessness left dark blood may wash away scar never fade least know somehow made mark dark light Nothing left nothing right x3 Nothing right x2 1
Little act little deed little mind breed breed kind Attitude gratitude let shit carry every day muthafuckas bury think dress act strange unchange word exchange Take deep breath try leave shit unsaid step stage unleash hatred Chorus Know fuck talkin Know fuck talkin Know fuck talkin Know fuck talkin way way Hypocrites politics democracy Governments overran hypocrisy go go business sellin soul ask forgiveness think everybody better come clean raise wrong happening Take deep breath try leave shit unsaid step stage unleash hatred Chorus Get back Get back muthafucker get back 1
spit cut tie hold tight time ha come realize thing loved despise life time life time picture wall around nt feel never felt way around nt feel spit justify time ha come realize thing loved despise life time life time picture wall around nt feel never felt way around nt feel end know faking try get back know faking end life time life time rise 1
rodeo rodeo riding several year ol Cheyenne Houston never cause much fear let tell one chill soul happens December snowin cold Oklahoma City building concrete toughest stock men gather compete point tallied stock ha brought got two hour cause start eight oclock Finals NFR Series sport Hey rookie take ten head got heart think tough cowboy find end final whistle blow stock pen coliseum quiet except sound cowboy getting ready workman ground cowboy ask got draw ol Necklace pluck old DoubleOught tension mounting crowd start pouring shiver go like cold cold wind hear horse comin runnin alleyway snortin blowin men shut sliding gate Finals NFR much time ago enough time Anthem grand entry going sit bronc ready wait hear chute gate open look gate horse come boiling blow roof come another kicking like curly wolf hear chute bos holler yelling crowd say one ahead better get screwed Finals NFR nod head daze horse go boiling run spurrs neck drag em mind blur eye seeing red flank catch slam back rump bang head somewhere background hear buzzer sound hand jerked riggin crash ground stumble foot stagger wall wonder really worth Finals NFR 1
Name: Lyrics, Length: 17367, dtype: int64
# Importing the libraries
from sklearn.svm import LinearSVC
# Split the TFIDFVectorizer data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(tfidf_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize LinearSVC model
linear_svc_model_tfidf = LinearSVC(max_iter=10000)
# Train the model
linear_svc_model_tfidf.fit(X_train, y_train)
# Make predictions on the test set
y_pred_tfidf_svc = linear_svc_model_tfidf.predict(X_test)
# Print the classification report
print("Classification Report for LinearSVC with TFIDFVectorizer:\n", classification_report(y_test, y_pred_tfidf_svc))
Classification Report for LinearSVC with TFIDFVectorizer:
precision recall f1-score support
0 0.48 0.35 0.41 362
1 0.84 0.75 0.80 431
2 0.68 0.74 0.71 999
3 0.71 0.75 0.73 1039
4 0.51 0.50 0.50 995
accuracy 0.65 3826
macro avg 0.64 0.62 0.63 3826
weighted avg 0.64 0.65 0.64 3826
# Trying SGD Model for word2vec
# Importing the SGD Classifier
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import RandomizedSearchCV # Import RandomizedSearchCV
# Split the Word2Vec mean vectors into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(word2vec_mean_vectors, genre_encoded, test_size=0.2, random_state=42)
from sklearn.metrics import classification_report
# Hyperparameters to search (reduced grid)
param_grid = {
'alpha': [1e-3, 1e-2], # Reduced number of options
'max_iter': [1000, 5000], # Reduced number of options
'penalty': ['l2'], # Keeping only 'l2'
'loss': ['hinge'] # Keeping only 'hinge'
}
# Create a RandomizedSearchCV object with the SGD Classifier (you can switch to GridSearchCV if desired)
# Here, n_iter is set to 4, so only 4 random combinations will be tried
random_search_word2vec_sgd = RandomizedSearchCV(SGDClassifier(), param_grid, n_iter=4, cv=5, verbose=1, n_jobs=-1)
# Fit the model to the training data
random_search_word2vec_sgd.fit(X_train, y_train)
# Print the best parameters and score
print("Best parameters found:", random_search_word2vec_sgd.best_params_)
# Make predictions on the test set using the best model
y_pred_word2vec_sgd_random = random_search_word2vec_sgd.predict(X_test)
# Print the classification report for the model with the best hyperparameters
print("Classification Report for SGD Classifier with Word2Vec (RandomizedSearchCV):\n", classification_report(y_test, y_pred_word2vec_sgd_random))
Fitting 5 folds for each of 4 candidates, totalling 20 fits
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
Best parameters found: {'penalty': 'l2', 'max_iter': 1000, 'loss': 'hinge', 'alpha': 0.001}
Classification Report for SGD Classifier with Word2Vec (RandomizedSearchCV):
precision recall f1-score support
0 0.24 0.11 0.15 362
1 0.51 0.78 0.62 431
2 0.52 0.77 0.62 999
3 0.63 0.76 0.69 1039
4 0.57 0.16 0.25 995
accuracy 0.55 3826
macro avg 0.49 0.52 0.47 3826
weighted avg 0.54 0.55 0.50 3826
# Split the GoogleNews Word2Vec mean vectors into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(google_w2v_mean_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize SGD Classifier
sgd_model_google_w2v = SGDClassifier(loss='hinge', max_iter=10000)
# Train the model
sgd_model_google_w2v.fit(X_train, y_train)
# Make predictions on the test set
y_pred_google_w2v_sgd = sgd_model_google_w2v.predict(X_test)
# Print the classification report
print("Classification Report for SGD Classifier with GoogleNews Word2Vec:\n", classification_report(y_test, y_pred_google_w2v_sgd))
Classification Report for SGD Classifier with GoogleNews Word2Vec:
precision recall f1-score support
0 0.57 0.07 0.12 362
1 0.68 0.75 0.71 431
2 0.59 0.75 0.66 999
3 0.67 0.78 0.72 1039
4 0.49 0.41 0.44 995
accuracy 0.60 3826
macro avg 0.60 0.55 0.53 3826
weighted avg 0.59 0.60 0.58 3826
from sklearn.ensemble import RandomForestClassifier
# Split the CountVectorizer data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(count_vectors,genre_encoded, test_size=0.2, random_state=42)
# Initialize Random Forest Classifier with modified hyperparameters
rf_model_count = RandomForestClassifier(n_estimators=50, max_depth=10, max_features='sqrt', random_state=42)
# Train the model
rf_model_count.fit(X_train, y_train)
# Make predictions on the test set
y_pred_count_rf = rf_model_count.predict(X_test)
# Print the classification report
print("Classification Report for Random Forest with CountVectorizer:\n", classification_report(y_test, y_pred_count_rf))
Classification Report for Random Forest with CountVectorizer:
precision recall f1-score support
0 0.00 0.00 0.00 362
1 0.87 0.68 0.76 431
2 0.50 0.83 0.62 999
3 0.63 0.72 0.67 1039
4 0.48 0.31 0.37 995
accuracy 0.57 3826
macro avg 0.50 0.51 0.49 3826
weighted avg 0.52 0.57 0.53 3826
/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
# Applying the Random Forest on Tf-IDF Vectorizer
# Split the TFIDFVectorizer data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(tfidf_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize Random Forest Classifier with reduced number of trees and limited tree depth
rf_model_tfidf = RandomForestClassifier(n_estimators=50, max_depth=10, max_features='sqrt', random_state=42)
# Train the model
rf_model_tfidf.fit(X_train, y_train)
# Make predictions on the test set
y_pred_tfidf_rf = rf_model_tfidf.predict(X_test)
# Print the classification report
print("Classification Report for Random Forest with TFIDFVectorizer:\n", classification_report(y_test, y_pred_tfidf_rf))
Classification Report for Random Forest with TFIDFVectorizer:
precision recall f1-score support
0 0.00 0.00 0.00 362
1 0.88 0.68 0.77 431
2 0.52 0.79 0.63 999
3 0.60 0.76 0.67 1039
4 0.48 0.32 0.39 995
accuracy 0.57 3826
macro avg 0.50 0.51 0.49 3826
weighted avg 0.52 0.57 0.53 3826
/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
# Applying the Random Forest on Word2Vec
# Split the Word2Vec mean vectors into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(word2vec_mean_vectors, genre_encoded , test_size=0.2, random_state=42)
# Initialize Random Forest Classifier with reduced number of trees and limited tree depth
rf_model_word2vec = RandomForestClassifier(n_estimators=50, max_depth=10, max_features='sqrt', random_state=42)
# Train the model
rf_model_word2vec.fit(X_train, y_train)
# Make predictions on the test set
y_pred_word2vec_rf = rf_model_word2vec.predict(X_test)
# Print the classification report
print("Classification Report for Random Forest with Word2Vec:\n", classification_report(y_test, y_pred_word2vec_rf))
Classification Report for Random Forest with Word2Vec:
precision recall f1-score support
0 0.50 0.01 0.02 362
1 0.78 0.66 0.71 431
2 0.67 0.72 0.69 999
3 0.66 0.71 0.69 1039
4 0.45 0.57 0.50 995
accuracy 0.60 3826
macro avg 0.61 0.53 0.52 3826
weighted avg 0.61 0.60 0.58 3826
# Applying the Random Forest on GoogleNews Word2Vec
# Split the GoogleNews Word2Vec mean vectors into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(google_w2v_mean_vectors, genre_encoded, test_size=0.2, random_state=42)
# Initialize Random Forest Classifier with reduced number of trees and limited tree depth
rf_model_google_w2v = RandomForestClassifier(n_estimators=50, max_depth=10, max_features='sqrt', random_state=42)
# Train the model
rf_model_google_w2v.fit(X_train, y_train)
# Make predictions on the test set
y_pred_google_w2v_rf = rf_model_google_w2v.predict(X_test)
# Print the classification report
print("Classification Report for Random Forest with GoogleNews Word2Vec:\n", classification_report(y_test, y_pred_google_w2v_rf))
Classification Report for Random Forest with GoogleNews Word2Vec:
precision recall f1-score support
0 0.00 0.00 0.00 362
1 0.76 0.63 0.69 431
2 0.64 0.71 0.67 999
3 0.66 0.74 0.69 1039
4 0.44 0.52 0.48 995
accuracy 0.59 3826
macro avg 0.50 0.52 0.51 3826
weighted avg 0.54 0.59 0.57 3826
from sklearn.metrics import accuracy_score
# Example accuracy calculations (replace the prediction variables with the correct ones from your models)
accuracy_rf_count = accuracy_score(y_test, y_pred_count_rf)
accuracy_rf_tfidf = accuracy_score(y_test, y_pred_tfidf_rf)
accuracy_rf_word2vec = accuracy_score(y_test, y_pred_word2vec_rf)
accuracy_rf_google_w2v = accuracy_score(y_test, y_pred_google_w2v_rf)
accuracy_svc_count = accuracy_score(y_test,y_pred_count_sgd)
accuracy_svc_tfidf = accuracy_score(y_test, y_pred_tfidf_svc)
accuracy_svc_word2vec = accuracy_score(y_test, y_pred_word2vec_sgd_random)
accuracy_svc_google_w2v = accuracy_score(y_test, y_pred_google_w2v_sgd)
accuracy_lr_count = accuracy_score(y_test, y_pred_count)
accuracy_lr_tfidf = accuracy_score(y_test, y_pred_tfidf)
accuracy_lr_word2vec = accuracy_score(y_test, y_pred_word2vec)
accuracy_lr_google_w2v = accuracy_score(y_test, y_pred_google_w2v)
import plotly.express as px
import pandas as pd
import plotly.io as pio
pio.renderers.default='notebook'
# Prepare data as a list of dictionaries
data = [
{'Model': 'Random Forest', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_rf_count},
{'Model': 'Random Forest', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_rf_tfidf},
{'Model': 'Random Forest', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_rf_word2vec},
{'Model': 'Random Forest', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_rf_google_w2v},
{'Model': 'SVM', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_svc_count},
{'Model': 'SVM', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_svc_tfidf},
{'Model': 'SVM', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_svc_word2vec},
{'Model': 'SVM', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_svc_google_w2v},
{'Model': 'Logistic Regression', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_lr_count},
{'Model': 'Logistic Regression', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_lr_tfidf},
{'Model': 'Logistic Regression', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_lr_word2vec},
{'Model': 'Logistic Regression', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_lr_google_w2v},
]
# Convert to DataFrame
df_plot = pd.DataFrame(data)
# Create a bar plot
fig = px.bar(df_plot, x='Model', y='Accuracy', color='Vectorizer', barmode='group',
title='Comparison of Accuracy for Different Models and Vectorizers')
# Show the plot
fig.show()
import plotly.express as px
import pandas as pd
import plotly.io as pio
pio.renderers.default='notebook'
# Prepare data as a list of dictionaries
data = [
# Random Forest
{'Model': 'Random Forest', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_rf_count},
{'Model': 'Random Forest', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_rf_tfidf},
{'Model': 'Random Forest', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_rf_word2vec},
{'Model': 'Random Forest', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_rf_google_w2v},
# SVM
{'Model': 'SVM', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_svc_count},
{'Model': 'SVM', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_svc_tfidf},
{'Model': 'SVM', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_svc_word2vec},
{'Model': 'SVM', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_svc_google_w2v},
# Logistic Regression
{'Model': 'Logistic Regression', 'Vectorizer': 'CountVectorizer', 'Accuracy': accuracy_lr_count},
{'Model': 'Logistic Regression', 'Vectorizer': 'TFIDFVectorizer', 'Accuracy': accuracy_lr_tfidf},
{'Model': 'Logistic Regression', 'Vectorizer': 'Word2Vec', 'Accuracy': accuracy_lr_word2vec},
{'Model': 'Logistic Regression', 'Vectorizer': 'GoogleNews Word2Vec', 'Accuracy': accuracy_lr_google_w2v},
]
# Convert to DataFrame
df_plot = pd.DataFrame(data)
# Create a sunburst plot
fig = px.sunburst(df_plot, path=['Model', 'Vectorizer'], values='Accuracy',
title='Comparison of Accuracy for Different Models and Vectorizers',
color='Accuracy', # Color by accuracy
color_continuous_scale='viridis',template='simple_white')
# Show the plot
fig.show()